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This project presents the SDM-RDFizer, an interpreter of mapping rules that allows the transformation of (un)structured data into RDF knowledge graphs. The current version of the SDM-RDFizer assumes mapping rules are defined in the RDF Mapping Language (RML) by Dimou et al.

Project description

SDM-RDFizer

This project presents the SDM-RDFizer, an interpreter of mapping rules that allows the transformation of (un)structured data into RDF knowledge graphs. The current version of the SDM-RDFizer assumes mapping rules are defined in the RDF Mapping Language (RML) by Dimou et al. The SDM-RDFizer implements optimized data structures and relational algebra operators that enable an efficient execution of RML triple maps even in the presence of Big data. SDM-RDFizer is able to process data from Heterogeneous data sources (CSV, JSON, RDB, XML).

SDM-RDFizer workflow

The results of the execution of SDM-RDFizer has been described in the following research reports:

  • Enrique Iglesias, Samaneh Jozashoori, David Chaves-Fraga, Diego Collarana, and Maria-Esther Vidal. 2020. SDM-RDFizer: An RML Interpreter for the Efficient Creation of RDF Knowledge Graphs. The 29th ACM International Conference on Information and Knowledge Management (CIKM ’20).

  • Samaneh Jozashoori, David Chaves-Fraga, Enrique Iglesias, Oscar Corcho, and Maria-Esther Vidal. 2020. FunMap: Efficient Execution of Functional Mappings for Knowledge Graph Creation. The 19th International Semantic Web Conference - Research Track (ISWC 2020).

  • Samaneh Jozashoori and Maria-Esther Vidal. MapSDI: A Scaled-up Semantic Data Integrationframework for Knowledge Graph Creation. The 27th International Conference on Cooperative Information Systems (CoopIS 2019).

  • David Chaves-Fraga, Kemele M. Endris, Enrique Iglesias, Oscar Corcho, and Maria-Esther Vidal. What are the Parameters that Affect the Construction of a Knowledge Graph?. The 18th International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE 2019).

  • David Chaves-Fraga, Antón Adolfo, Jhon Toledo, and Oscar Corcho. ONETT: Systematic Knowledge Graph Generation for National Access Points. The 1st International Workshop on Semantics for Transport co-located with SEMANTiCS 2019.

  • David Chaves-Fraga, Freddy Priyatna, Andrea Cimmino, Jhon Toledo, Edna Ruckhaus, and Oscar Corcho. GTFS-Madrid-Bench: A benchmark for virtual knowledge graph access in the transport domain. Journal of Web Semantics, 2020.

Additional References:

  • Dimou et al. 2014. Dimou, A., Sande, M.V., Colpaert, P., Verborgh, R., Mannens, E., de Walle, R.V.:RML: A generic language for integrated RDF mappings of heterogeneous data. In:Proceedings of the Workshop on Linked Data on the Web co-located with the 23rdInternational World Wide Web Conference (WWW 2014)

Projects where the SDM-RDFizer has been used

The SDM-RDFizer is used in the creation of the knowledge graphs of EU H2020 projects and national projects where the Scientific Data Management group participates. These projects include:

The SDM-RDFizer is also used in EU H2020, EIT-Digital and Spanish national projects where the Ontology Engineering Group (Technical University of Madrid) participates. These projects, mainly focused on the transportation and smart cities domain, include:

  • H2020 - SPRINT (http://sprint-transport.eu/): performance and scalability to test a semantic architecture for the Interoperability Framework on Transport across Europe.
  • EIT-SNAP (https://www.snap-project.eu/): innovation project on the application of semantic technologies for national access points.
  • Open Cities (https://ciudades-abiertas.es/): national project on creating common and shared vocabularies for Spanish Cities
  • Drugs4Covid (https://drugs4covid.oeg.fi.upm.es/): NLP annotations and metadata from more than 60,000 scientific papers about COVID viruses are integrated in a KG with almost 44M of facts (triples). SDM-RDFizer was used for creating this KG.

Installing and Running the SDM-RDFizer

From PyPI (https://pypi.org/project/rdfizer/):

python3 -m pip install rdfizer
python3 -m rdfizer -c /path/to/config/file

From Github/Docker: Visit the wiki of the repository to learn how to install and run the SDM-RDFizer. You can also take a look to our demo at: https://www.youtube.com/watch?v=DpH_57M1uOE

Version

3.3.2.2

RML-Test Cases

See the results of the SDM-RDFizer over the RML test-cases at the RML Implementation Report. Last test date: 08/06/2020

Experimental Evaluations

See the results of the experimental evaluations of SDM-RDFizer at SDM-RDFizer-Experiments repository

License

This work is licensed under Apache 2.0

Authors

The SDM-RDFizer has been developed by members of the Scientific Data Management Group at TIB, as an ongoing research effort. The development is coordinated and supervised by Maria-Esther Vidal (maria.vidal@tib.eu). We strongly encourage you to please report any issues you have with the SDM-RDFizer. You can do that over our contact email or creating a new issue here on Github. The SDM-RDFizer has been implemented by Enrique Iglesias (current version, s6enigle@uni-bonn.de) and Guillermo Betancourt (version 0.1, guillermojbetancourt@gmail.com) under the supervision of David Chaves-Fraga (dchaves@fi.upm.es), Samaneh Jozashoori (samaneh.jozashoori@tib.eu), and Kemele Endris (kemele.endris@tib.eu)

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